USING DEMETRA+ IN DAILY WORK SAUG – Luxembourg, 16 October 2012 Enrico INFANTE, Eurostat Unit B1:...
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Transcript of USING DEMETRA+ IN DAILY WORK SAUG – Luxembourg, 16 October 2012 Enrico INFANTE, Eurostat Unit B1:...
USING DEMETRA+ IN DAILY WORK
SAUG – Luxembourg, 16 October 2012Enrico INFANTE, EurostatUnit B1: Quality, Methodology and Research
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Summary
Price Series– Price Volatility Analysis
Production Series– Seasonal Adjustment– SARIMA Forecasts– Current vs. Concurrent Adjustment– Direct vs. Indirect approach
• IB test– In the future…
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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The study is conducted on Italian food and agricultural data
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Price Volatility Analysis
PRICE SERIES
Price Volatility Analysis
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How many series?
SectorPrice Volatility
Analysis
Milk 1
Butchering 10
Agro-food 48
Ichthyic 12
Total 71
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Price Volatility Analysis
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Price Volatility Analysis
The basic idea is to check whether the price of a determined product in a determined period is outside the expected trend. The procedure follows three main steps:
1.Model the series without the last three observations using a SARIMA(p,d,q)(P,D,Q)
2.Using the model identified during step 1 to produce forecast intervals for the three observations not considered in step 1
3.Check whether the price observed is inside or outside the forecast interval
If the price observed is outside the interval, then there is high volatility
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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The procedure is the follow
SAUG – Luxembourg, 16 October 2012 Enrico Infante
heVARzhx nn 2ˆ
Price Series
SARIMA model without
3 obs.
Forecast intervals
Detect the volatility degree
Price Volatility Analysis
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Example: Parmigiano Reggiano and Grana Padano
There is low volatility for the "Grana Padano", as the observed price is in the forecasted interval
There is high volatility for the "Parmigiano Reggiano", as the observed price is not within the forecasted interval
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Price Volatility Analysis
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Special cases (e.g. fruit)
In some cases (typically for the fruit series) there are data just for a certain period of the year. In these cases the available data are considered consecutive, and no seasonal and/or calendar effects are considered
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Price Volatility Analysis
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The study is conducted on Italian food and agricultural data
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Seasonal Adjustment
Forecast
Direct/Indirect approach
Current/Concurrent AdjustmentPRODUCTION
SERIES
Seasonal Adjustment
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How many series?
Sector SA and Forecast
Milk 5
Butchering 21
Ichthyic 1
Import/Export Agro-food 99
Import/Export Ichthyic 20
Total 145
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Seasonal Adjustment
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Seasonal Adjustment
The series are seasonally adjusted following the ESS guidelines
A knowledge of the sectors is a key part for modelling outliers, calendar effects, etc.
The series are modelled using the TRAMO/SEATS modules of Demetra+
The models detected from the automatic procedure (RSA4) are modified, when necessary, in order to get better results
If necessary, the earliest years are removed from the model
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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SARIMA Forecasts
The study focuses on the forecasts of the series The first step is to identify the SARIMA model
Demetra+ forecasts The forecasts are sometimes "adjusted" basing on
qualitative information Example: Milk
The total milk produced in Italy is more or less the same amount for every production year (from April to March). So the values obtained from Demetra+ are adjusted in order to get the same total (10883,073)
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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Current vs. Concurrent Adjustment
The way in which Seasonal Adjustment is carried out has implications for the revisions of seasonally adjusted data
There are monthly series that are updated every quarter, so each quarter there are 3 new observations
Usually there are no revisions on the series, and even if there are some, they are very small
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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Current vs. Concurrent Adjustment
A partial Concurrent Adjustment strategy is chosen, with an update of the parameters coefficient and outliers in the last year
SAUG – Luxembourg, 16 October 2012 Enrico Infante
The main issue arises when the chosen model does not produce good forecasts, even if the model seems to be good. In that case the model is re-estimated in order to get better results in terms of forecasts
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Direct vs. Indirect approach
Some series are aggregated at different levels In order to decide which approach to follow, an a priori
test has been used The main advantage of using an a priori test is that the
choice is made on statistical bases without running any seasonal adjustment procedure
The test used is the so-called IB test, that is in line with the ESS guidelines on Seasonal Adjustment: when the series composing the aggregate have common similar seasonal patterns, a Direct approach would be used
The test is performed using R scripts
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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Direct vs. Indirect approach – IB test
The basic idea of the IB test is stated in Infante and Buono (2012)
The classic test for moving seasonality is based on a 2-way ANOVA test, where the two factors are the time frequency (usually months or quarters) and the years. This test is based on a 3-way ANOVA model, where the three factors are the time frequency, the years and the series
The variables tested are the de-trended series, where the trend is determined by applying a Hodrick-Prescott filter for each series
The notation SI is kept for remarking the fact that it is a de-trended series
HPijkijkijk TXSI
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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Direct vs. Indirect approach – IB test
The null hypothesis is made by taking into consideration that there is no change in seasonality over the series
The test statistic is the ratio of the between series variance and the residual variance, and it follows a Fisher-Snedecor distribution with (S-1) and (MNS-1)-(M-1)-(N-1)-(S-1) degrees of freedom
Rejecting the null hypothesis is to say that the pure Direct Approach should be avoided, and an Indirect Approach should be considered
1111;12
2
~ SNMMNSSR
ST F
S
SF
ScccH 210 :
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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Direct vs. Indirect approach – IB test
An example: butchering pork
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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Direct vs. Indirect approach – IB test
An example: butchering pork
SAUG – Luxembourg, 16 October 2012 Enrico Infante
VAR Mean Square df
Months 4.818.439.366 11
Years 1.135.483.533 10
Series 141.362.592.258 2
Residual 1.054.706.450 384
134.0303F
0000.0 valueP
There is no evidence of common seasonal patterns between the series at 5 per cent level
An Indirect approach is recommended
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In the future…
From Demetra+ to Jdemetra+!!!
SAUG – Luxembourg, 16 October 2012 Enrico Infante
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In the future…
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Assessment for residual seasonality
•Additive aggregate
•What if the aggregation function is different?
Test a priori
Java code
(older version)
R code
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Questions
Many thanks for your attention!!!
SAUG – Luxembourg, 16 October 2012 Enrico Infante
Your questions and comments are very welcome!